|تعداد مشاهده مقاله||7,209,809|
|تعداد دریافت فایل اصل مقاله||5,601,483|
Monitoring and prediction of drought using TIBI fuzzy index in Iran
|Caspian Journal of Environmental Sciences|
|دوره 18، شماره 3، مهر 2020، صفحه 237-250 اصل مقاله (1.58 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22124/cjes.2020.4136|
|Behrouz Sobhani؛ Vahid Safarianzengir*|
|Department of Physical Geography, Climatology, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, Iran|
|The drought phenomenon is not specific to the region, affecting different parts of the world. One of these areas is Iran in Southwest Asia, suffering from this phenomenon in recent years. The purpose of this study was to model, analyze and predict the drought in Iran. So that, climatic parameters (precipitation, temperature, sunshine, minimum relative humidity and wind speed) were used at 30 stations during 29 years (1990-2018). For modelling the TIBI fuzzy index, at first, four indicators (SET, SPI, SEB, and MCZI) were been fuzzy in MATLAB software. Then the indices were compared and the TOPSIS model was used for prioritizing areas involved drought, followed by employing ANFIS adaptive artificial neural network model for predicting drought. Results showed that the new fuzzy index TIBI for classifying drought reflected four above indicators with high accuracy. Of these five climatic parameters used in this study, the temperature and precipitation exhibited the most impact on the fluctuation of drought severity. The severity of drought was more based on 6-month scale modelling than on 12-month one. The highest rate of drought occurrence was found at the Bandar Abbas station with 24.30% on a 12-month scale, and while the lowest was at the Shahrekord station with 0.36% on a six-month scale. Based on ANFIS model and TIBI fuzzy index, Bandar Abbas, Bushehr and Zahedan stations were more encountered ones to drought due to the TIBI index of 0.62, 0.96 and 0.97 respectively. According to the results in both 6- and 12-month scales, the southern regions of Iran were more severely affected by drought, which requires suitable water management in these areas.|
|Statistical evaluation؛ TIBI index؛ Fuzzy؛ Drought؛ ANFIS|
Ahmadzadeh, G, Majid, L, & Kourosh, M 2010, Comparison of artificial intelligence systems (ANN and ANFIS) in estimating the rate of transpiration of reference plants in very dry regions of Iran.Journal of Water and Soil, 2: 679-689. [In Persian].
Alam, NM, Sharma, GC, Moreira, E, Jana, C, Mishra, PK, Sharma, NK & Mandal, D 2017, Evaluation of drought using SPEI drought class transitions and log-linear models for different agro-ecological regions of India. Physics and Chemistry of the Earth, 100: 31-43.
Alizadeh, Sh, Mohammadi, H & Kordvani, P 2017, Modeling the Dispersion of Drought Caused by Climate Change in Iran Using Dynamic System. Land Expansion, 9: 169-188. [In Persian].
Amazzal, A, Ait-Talborjt, E, Hermas, J & Hafidi, N 2020, Importance of hydrological parameters in the distribution of planktonic eggs and larvae in an upwelling zone (Imessouane Bay, Moroccan Atlantic Coast). Caspian Journal of Environmental Sciences, 18: 1-12.
Ansari, H, Davari, K & Sanaeenejad, SH 2010, Drought monitoring using SEPI standardized rainfall and sedimentation index, developed on the basis of fuzzy logic. Journal of Soil and Water (Agricultural Sciences and Technology), 1: 38-52.
Asgharioskoee, M 2002, Application of neural networks in time series forecasting. Journal of Economic Researches of Iran, 4: 79-99. [In Persian].
Azizi, A, Krika, A & Krika, F 2020, Heavy metal bioaccumulation and distribution in Typha latifolia and Arundo donax: implication for phytoremediation. Caspian Journal of Environmental Sciences, 18: 21-29.
Babayan, E, kazanedari, L, Abbasi, F, modirian, R, Karimian, M & Melboji, S 2018, Monthly drought forecasting in the southwest drainage basin using CFSv.2 model. Iranian Water Resources Research, 14: 133-145. [In Persian].
Barqi, H, bazrafshan, J & Shayan, M 2018, Analysis and identification of drought effects on rural areas (Case study: Chahgah village, Fereydounshahr). Journal of Environmental Risks, 7: 141-160. [In Persian].
Bayazidi, M 2018, Drought evaluation of synoptic stations in the west of Iran using the Hirbst method and comparative neuro-fuzzy model. Iranian Water Resources Research, 14: 278-284. [In Persian].
Damavandi, AA, Rahimi, M, Yazdani, MR & Norouzi, AA 2016, Field monitoring of agricultural drought through time series of NDVI and LST indicators. MODIS data (Case study: Markazi Province, Iran). Geographic Information Research (Sepehr), 25: 115-126. [In Persian].
Ekhtiarikhajeh, S & Dinpazhoh, Y 2018, Application of the effective drought index (EDI) for studying dry periods (Tabriz, Bandar Anzali and Zahedan stations). Irrigation Sciences and Engineering, 11: 33-145.
Fanni, Z, Khalilalahi, HA, Sajjadi, J & Falsleman, M 2016, Analysis of the causes and consequences of drought in South Khorasan Province and Birjand. Journal of Planning and Space Design, 20: 175-200. [In Persian].
Fathi-Zadeh, H, Gholaminia, A, Mobin, M & Soodyzizadeh, H 2017, Investigating the Relationship between Meteorological Drought and Solar Variables in Some Iranian Standards. Environmental Hazards, 17: 63-87. [In Persian].
Gebremeskel, G, Tang, Q & Sun, S 2019, Droughts in East Africa: Causes, impacts and resilience. Earth-Science Reviews, 124: 68-96.
Gholamali, M, Younes, K, Esmaeil, H & Fatemeh, T 2011, Assessment of geostatistical methods for spatial analysis of SPI and EDI drought Indices. World Applied Sciences Journal, 15: 474-482.
Ghorbani, K, Valizadeh I & Bararkhranpour, S 2018, Investigation of spatial variations of spatial variance of SPEI drought index in Iran. Desert Management Journal, 11: 38-25.
Haddadi, H & Heidari, H 2015, Detection of the effect of precipitation fluctuations on surface water flood in Lake Urmia catchment basin. Geography and Environmental Planning, 57: 247-262. [In Persian].
Hao, Z, Hao, F, Singh, V, Xia, Y & Xinyishen, O 2016, A theoretical drought classification method for the multivariate drought index based on distribution properties of standardized drought indices. Advances in water resources, 14: 240-247.
Hartman, E, Keeler, JD & Kowalski, JM 1990, Layered neural networks with Gaussian hidden units as universal approximations. Neural Computation, 2: 210-215.
Hejabi, S, Irannejad, P & Bazrafshan, J 2012, Adjustment of the Palmer Drought Extreme Index (PDSI) Based on the Marine-Drought Level Interaction Scheme (ALSIS) in the Karkheh catchment basin. Iranian Journal of Water Resources, 14: 204-219. [In Persian].
Hejazizazadeh, Z & Javiyazadeh, S 2019, Analysis of Drought Spatial Statistics in Iran. Journal of Applied Geosciences Research, 19: 251-277. [In Persian].
Huanga, S, Huanga, Q, Changa, J, Zhua, Y & Lengb, G 2015, Drought structure based on a nonparametric multivariate standardized drought index across the Yellow River basin China. Journal of Hydrology, 530: 127-136.
Jafari, GhH, Bakhtiari, F & Dostkamian, M 2018, Analysis of the spatial association of droughts with the watershed water flow of Ghezel Ozan basin. Geography and Development, 15: 79-94.
Jafarnejad, A & Kia, SM 2010, Fuzzy Logic in MATLAB. Kian Rayaneh Sabz publication, 157-180.
James, H, Stagge, a, IreneKohn, b, Lena, M, Tallaksen, A & Kerstin, S 2015, Modeling drought impact occurrence based on meteorological drought indices in Europe. Journal of Hydrology, 530: 37-50.
Jandarmian, I, Shakiba, A & Nasseri, H 2015, Study of Drought Status and Its Relationship with Quantitative and Qualitative Changes in Groundwater in Sarab Plain. International Conference on Development, Focusing on Agriculture, Environment and Tourism, Iran, Tabriz, 16-17. [In Persian].
Jinum, M & Jeonbin, K 2017, Evaluatin historical drought charactristics simulated in Cordexast Asia against observations. International journal of climatology, 25: 32-43.
Kamasi, M, Mohammadi, M & Montaseri, H 2016, Drought prediction with SPI and EDI index using ANFIS modeling method in Kohgiluyeh and Boyerahmad province. Agricultural Meteorology Journal, 1: 36-47.
Keshtkai, S 2015, Drought Study in West Azarbaijan province with Spi and Gis Index. International Conference on Agricultural, Environment and Tourism, Iran, Tabriz, 16-19.
Khanjani, T, Ataei, M & Peyman, T 2016, Influence of Wind Speed on RBF Neural Network Based on Chaos Theory. Computational Intelligence in Electrical Engineering, 7: 87-96. [In Persian].
Kis, A, Rita, P & Judit, B 2017, Multi- model analysis of regional dry and wet condition for the Carpatian Region. International journal of climatology, 17: 4543-4560.
Konarkuhi, A SoleimanJahi, H, Falahi, S, Riahimadvar, H & Meshkat, Z 2010, Using the New Intelligent Fuzzy-Neural Recognition Inventory System (ANFIS) to predict the human cannibalization potential of human papillo virus. Journal of Arak University of Science and Technology, 13: 95-105. [In Persian].
Liu, M, X. Xianli, Y, sun, A, lexander & Kelin, W 2017, Decreasing spatial variability of drought in south west china during 1959-2013. International journal of climatology, 21: 4610-4619.
Makvandi, R, Maghsoudlo-Kamali, B & Mohammadfam, I 2012, Utilization of TOPSIS Multivariate Decision Making Model for Assessing the Environmental Consequences of Oil Refineries (Case Study: Khuzestan Extra Heavy Oil Refinery). Environmental Studies, 5: 77-86. [In Persian].
Malchovsky, Y 2007, Geographic Information System and Multi-criteria Decision Analysis, Translated by Akbar Parizgar. Ata Ghafari Flooded. Tehran. Publishing Side, 9: 543-563. [In Persian].
Marchanta, BP & Bloomfield, JP 2018, Spatio-temporal modelling of the status of groundwater droughts. Journal of Hydrology, 564: 397-413.
Mdehheb, Z, Elkihel, B, Bouamama, M, Hammouti, B & Delaunois, F 2020, The environmental management system and its application impacts on the business economy in the eastern region of Morocco. Caspian Journal of Environmental Sciences (CJES), 18: 13-20.
Mirzaee, F, Iraqi, Nezhad, Sh & Big-Haddad, A 2015, Development of WEAP Integrated Water Model Model for Drought Condition Modeling. Journal of Engineering and Watershed Management, 7: 85-97. [In Persian].
Modaresirad, A, Ghahramani, B, Khalili, D, Ghahramani, Z & Ahmadiardakani, S 2017, Integrated meteorological and hydrological drought model: A management tool for proactive water resources planning of semi-arid regions. Advances in Water Resources, 54: 336-353.
Montaseri, M & Amirataee, B 2015, Stochastic estimation of drought prevalence (Case study: Northwest of Iran). Journal of Civil and Environmental Engineering, 45: 12-26. [In Persian].
Montaseri, M, Norjo, A, Bahmanesh, J & Akbari, M 2018, Wet season and meteorological drought in southern basins of Lake Urmia. Ecoehydrology, 1: 189-202.
Moradi, H, Tayyi, M, Ghasemian, D, Chesghi, J & Bahari, R 2008, Simulation and analysis of the relationship between water and climate droughts using probabilistic models of Babol plain. Iran Watershed Association, 2: 71-74. [In Persian].
Nazmfar, H & Amina, A 2014, Measurement of spatial inequality in using educational indices by TOPSIS method (Case study: Khorestan Province). Two Chapters of Educational Planning Studies, 3: 115-134. [In Persian].
Nowrooz, a, Rostami, N & Jahangir, M 2018, The prediction of drought conditions during the period of 2018-2037 under a change-oriented approach (Case study: Ilam and Dehloran stations). Ecohydrology, 5: 977-991. [In Persian].
Parsamehr, AH & Khosravani, Z 2017, Determination of drought determination using multi-criteria decision making based on TOPSIS. Research on Pasture and Desert of Iran, 24: 16-29.
Qi, L, Guanlan, Z, Shahzad, A, Xiaopeng, W, Guodong, W, Zhenkuan, P & Jiahua, Z 2019, SPI-based drought simulation and prediction using ARMA-GARCH model. Applied Mathematics and Computation, 355: 96-107.
Qorbani, K, Walizadeh, I & Barkhranpour, S 2018, Investigation of spatial variations of spatial variance of SPEI drought variables in Iran. Desert Management Journal, 11: 25-38. [In Persian].
Quesada, B, Giuliano, M, Asarre, D, Rangecoft, S & Vanloon, A 2008, Hydrological change: Toward a consistent approach to assess changes on both floods and droughts. Advances in Water Resources, 5: 31-35.
Safarianzengir V, Sobhani, B 2020, Simulation and analysis of natural hazard phenomenon, drought in southwest of the Caspian Sea, IRAN. Carpathian Journal of Earth and Environmental Sciences,15: 127-136.
Safarianzengir, V, Sobhani, B, Asghari, S 2019, Modeling and monitoring of drought for forecasting it, to reduce natural hazards atmosphere in western and north western part of Iran, Iran. Air Qual Atmos Health, 6: 68-79.
Salahi, B & Mojtabapour, F 2016, Spatial analysis of climate drought in northwest of Iran using spatial correlation statistics. Journal of Environmental Spatial Spatial Analysis, 3: 1-20. [In Persian].
Salajeghe, A & Fathabadi, A 2009, Investigating the possibility of estimating the suspended load of Karaj River using fuzzy logic and neural network. Journal of Rangeland and Watershed Management, (Iranian Journal of Natural Resources), 2: 271-282.
Samidianfard, S & Asadi, I 2018, Projection of SPI drought index by multiple regression and supportive vector regression methods. Water and Soil Conservation, 6: 1-16.
Shamsniya, A, Pirmoradian, N & Amiri, N 2008, Drought modeling in Fars Province using Time Series Analysis. Geography and Planning, 28: 165-189.
Sobhani, B & Safarianzengir, V 2018, Investigating and predicting the risk of monthly rainfed exposure to horticultural and agricultural products in the northern strip of Iran (Golestan, Guilan and Mazandaran provinces). Journal of Environmental Spatial Analysis, 5: 125-144. [In Persian].
Sobhani, B & Safarianzengir, V 2019a, Modeling, monitoring and forecasting of drought in south and southwestern Iran, Iran. Modeling Earth Systems and Environment, 4: 89-101.
Sobhani, B & Safarianzengir, V 2019b, Investigation hazard effect of monthly ferrrin temperature on agricultural products in north bar of Iran. Iraqi Journal of Agricultural Sciences, 50: 320-330.
Sobhani, B & Safarianzengir, V 2020, Evaluation and zoning of environmental climatic parameters for tourism feasibility in northwestern Iran. located on the western border of Turkey. Modeling Earth Systems and Environment, https://doi.org/10.1007/s40808-020-00712-1.
Sobhani, B, GhafariGilandeh, A & Golvost, A 2015, Drought monitoring in Ardebil province using the developed SEPI index based on fuzzy logic. Journal of Applied Geosciences Research, 15: 51-72. [In Persian].
Sobhani, B, Jafarzadehaliabad, L & Safarianzengir, V 2020a, Investigating the effects of drought on the environment in northwestern province of Iran, Ardabil, using combined indices. Iran. Modelling Earth Systems and Environment, 9: 23-49.
Sobhani, B, Safarianzengir, V & Kianian, MK 2019a, Drought monitoring in the Lake Urmia basin in Iran. Arabian Journal of Geosciences, 12: 437-448.
Sobhani, B, Safarianzengir, V & Miridizaj, F 2019c, Feasibility study of potato cultivating of Ardabil province in Iran based on VIKOR model. Revue Agriculture, 10: 92 – 102.
Sobhani, B, Safarianzengir, V & Yazdani, MH 2020b, Modelling, evaluation and simulation of drought in Iran, southwest Asia. Journal of Earth System Science,129: 100.
Sobhani, B, Safarianzengir, V, Kianian & MK 2019b, Modeling, monitoring and prediction of drought in Iran. Iranian (Iranica) Journal of Energy and Environment, 10: 216-224. doi: 10.5829/ijee.2019.10.03.09
Sobhani, B, Safarianzengir, V, Kianian, MK 2018, Potentiometric mapping for wind turbine power plant installation, Guilan Province in Iran. Journal of Applied Sciences and Environmental Management, 22: 1363-1368.
Spinoni, j, Naumann, G, vogt, j & Barbosa, P 2015, The biggest drought events in Europe from 1950-2012. Journal of Hydrology: Regional, 3: 509-524.
Torabipour, H, Shahinejad, B & Dehghani, R 2018, Drought Estimation Using Smart Networks. Hydrogeomorphology, 14:179-197.
Touma, D, Ashfaq, M, Nayak, M, Kao, SC & Diffenbaugh, N 2015, A multi-model and multi-index evaluation of drought characteristics in the 21st century. Journal of Hydrology, 526: 196-207.
Wei, H, ZaiQing, C, Dongdong, Z & Guolin, F 2019, Drought loss assessment model for southwest China based on a hyperbolic tangent function. International Journal of Disaster Risk Reduction, 33: 477-484.
Zeinali, B & Safarianzengir, V 2017, Drought Monitoring in Urmia Lake Basin Using Fuzzy Index. Journal of Environmental Risks, 6: 37-62. [In Persian].
Zeinali, B, Asghari, S & Safarianzengir, V 2017, Drought monitoring and assessment of its prediction in Lake Urmia basin using SEPT and ANFIS model. Environmental Impact Analysis Spatial Analysis Journal, 4: 73-96. [In Persian].
Zelekei, T, Giorgi, T, Diro, F & Zaitchik, B 2017, Trend and periodicity of drought over Ethiopia. International Journal of Climatology, 65: 4733-4748.
Zolfaghari, H & Nourizamara, Z 2016, Application of drought index (CPEL) in determining proper variables for drought analysis in Iran. Journal of Spatial Analysis of Environmental Hazards, 3: 99-114 (In Persian).
Zolfagharpour, HR, Nowrouz, P, Mohseni‐Bandpei, A, Majlesi, M, Rafiee, M & Khalili, F 2020, Influences of temperature, waste size and residence time on the generation of polycyclic aromatic hydrocarbons during the fast pyrolysis of medical waste. Caspian Journal of Environmental Sciences, 18: 47-57.
تعداد مشاهده مقاله: 864
تعداد دریافت فایل اصل مقاله: 741